A Survey of the Quasi-3D Modeling of Wind Turbine Icing
Bibliographic record
Abstract
Wind turbine icing has been the subject of intensive research over the past two decades, primarily focusing on applying computational fluid dynamics (CFD) to 2D airfoil simulations for parametric analysis. As a result of blades’ airfoils deformation caused by icing, wind turbines experience a considerable decrease in aerodynamic performance resulting in a substantial loss of productivity. Due to the phenomenon’s complexity and high computational costs, a fully 3D simulation of the entire iced-up rotating turbine becomes infeasible, especially when dealing with several scenarios under various operating and weather conditions. The Quasi-3D steady-state simulation is a practical alternative method to assess power loss resulting from ice accretion on wind turbine blades. To some extent, this approach has been employed in several published studies showing a capability to estimate performance degradation throughout the generation of power curves for both clean and iced wind turbines. In this paper, applying the Quasi-3D simulation method on wind turbine icing was subject to a survey and in-depth analysis based on a comprehensive literature review. The review examines the results of the vast majority of recently published studies that have addressed this approach, summarizing the findings and bringing together research in this area to conclude with clear facts and details that enhance research on the estimation of wind turbine annual power production loss due to icing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".